Classical data values are single points in p-dimensional space; symbolic data values are
hypercubes (broadly defined) in p-dimensional space (and/or a cartesian product of p …

Covers everything readers need to know about clustering methodology for symbolic data—including
new methods and headings—while providing a focus on multi-valued list data, …

Increasingly, datasets are so large they must be summarized in some fashion so that the
resulting summary dataset is of a more manageable size, while still retaining as much …

When observations in large data sets are aggregated into smaller more manageable data
sizes, the resulting classifications of observations invariably involve symbolic data. In this …

Explores the application of bootstrap to problems that place unusual demands on the method.
The bootstrap method, introduced by Bradley Efron in 1973, is a nonparametric technique …

With the advent of computers, very large datasets have become routine. Standard statistical
methods don’t have the power or flexibility to analyse these efficiently, and extract the …

This article proposes a new approach to principal component analysis (PCA) for interval-valued
data. Unlike classical observations, which are represented by single points in p-…

Billard and Diday (2000) developed procedures for fitting a regression equation to symbolic
interval-valued data. The present paper compares that approach with several possible …

In 1987, Diday added a new dimension to data analysis with his fundamental paper introducing
the notions of symbolic data and their analyses. He and his colleagues, among others, …

With the advent of contemporary computers, datasets can be massively huge, too large for
direct analysis. One of the many approaches to this problem of size is to aggregate the data …